If you’ve been following our company blog for the past month or so, you know that we’ve been focusing on data analytics and what a powerful tool it can be to benefit fundraising. But maybe, like others I’ve been talking with recently, you feel that you’re not yet ready because your data isn’t pristine, or you don’t have enough data, or you just have too much else to do.

If you’re one of those folks who is feeling a little overwhelmed, I put together this brief video just for you. Really, all you need to get started in analytics are these 16 little fields. [Read more…]

If you’re a regular reader, you know that each month we feature special guests writing about their favorite topics. This month we welcome HBG Senior Researcher and member of the HBG Analytics team, Tara McMullen to share her thoughts about one of her favorite subjects!

Sometimes it’s hard to get started with a new program or type of technology because we don’t know what its power is. We don’t know what it can DO, so we stick with the old familiar way of doing things. But these days, doing things the same old way can leave your progress lagging and your program looking a little old-fashioned.

Sometimes the basic principles are good, they just need a little updating.

Maybe you are thinking about undertaking a campaign and aren’t sure if you have the critical mass or the right prospects to meet your goal.

Or maybe you are looking to create a prospect management system, and want a way to sort prospects into various stages in the pipeline.

Or maybe you are trying to find new potential volunteers for your board. [Read more…]

In a world where computer processing power doubles every 18 months and disk storage density doubles every year, no matter what industry we’re in, we’ve all got a lot of valuable data sitting idle. It’s just sitting there inside that computer-shell, waiting to be discovered.

Even less-than-perfect data situations (a crummy database, or records that haven’t been updated regularly, for example) have important stories to tell us about what we’ve done well (or badly) and where opportunity lies. You don’t need Big Data to get big answers. [Read more…]

How are top-performing organizations pulling away from their peers? In many cases, it’s through an understanding and clever usage of analytics. This week we welcome HBG Senior Researcher and analytics student and practitioner, Heather Willis, to The Intelligent Edge. Heather shares some of the latest studies with tips on the most important things organizations should do to take advantage of the data available to them.

So: are you a Pacesetter or a Dabbler? What do I mean by that? As you probably already know, we are in the midst of significant change in how we deal with and use the massive amount of data that is being created and collected each day. [Read more…]

What are the best representations of information that you’ve ever seen? For most people, they’re the ones that are the simplest to understand. The ones that display information clearly and move people to action. Or inaction:

Even in a different language, if we understand the context, the message is still clear.

That’s what good data visualization does. It takes information and lays it out in the simplest of terms to get across what needs to be communicated.

KISS

There’s no getting around the fact that data analytics is intimidating for most of us, and that it produces a lot of really interesting factoids that can move people to action. But when you’re trying to get across complex information, there are still ways to keep messaging simple. In fact, the more complex the information, the more imperative it is to KISS (Keep It Simple, Stupid). Or, in the words of Joey Cherdarchuk, director of operations at DarkHorse Analytics, Data Looks Better Naked.

THE MASTER AT WORK

If you haven’t seen Hans Rosling’s fascinating TED Talks using his free software GapMinder or watched his fascinating holographic data visualization on YouTube, you really should take the time to be impressed. And inspired.

Even if you’re not a data analytics person, watching Dr. Rosling will help you think about presenting information better, whether you’re a frontline fundraiser, researcher, social media guru, consultant or …just about anyone. Okay, go watch those two videos and then come back and we’ll talk more about available resources for visualizing data.

RESOURCES

Now that you’re inspired to do something different, let’s talk about what’s available for manipulating and showing off the great conclusions (or continuing questions) that your data is begging you to share.

Enter the graph, the moving chart, the GIF, the mini movie, the podcast, and the interactive illustration, to name a few, all made possible by recent entries into the market from companies including (in alpha order) Advizor Solutions, a flexible visualization tool that is especially good for looking at annual giving results and gift officer portfolio performance; Factary, whose product Atom allows you to visualize relationships between people, organizations and entities; Rapid Insight, a modeling and illustration package; and Tableau Software, an interactive visualization tool that allows for on-the-fly graphic shifting and drill-down-into-the-data capability.

What’s free?

NodeXL, which adheres to Excel like an elbow-length glove to allow you to manipulate and visualize relationships and social media interactions. Hans Rosling’s Big Data manipulator, GapMinder, which he uses in the aforementioned videos, is also completely free to download. And there are lots of others.

Where are some examples of visualizations?

Here is a terrific collection of resources from Andy Kirk, a UK-based freelance data visualization design consultant. And Mike Bostock of The New York Times has pulled together a great compilation of compelling visualization tools.

Finally, we’d like to leave you with a quote by David Schmitt, head of Performance Strategy and Planning Analytics in the finance division at the Intercontinental Hotels Group (IHG). Schmitt’s team is responsible for communicating information about IHG’s financial performance in a way that is entertaining, compelling and clear. You wouldn’t think that corporate financials would lend themselves to music videos, but that’s one of the tools Schmitt’s group has used to visualized data. For him, “data isn’t the point; numbers aren’t the point—it’s about the idea.”

It looks like our Excel pie chart days are probably over, thank goodness. We’d love to hear about any tools, resources or sites that you’ve found to help you visualize data, too! Please share them here in Comments, so that all of us can check them out.

Chances are good if you are in the fundraising field that you have heard the term “fundraising analytics.” You’ve probably also heard the terms “data mining,” “donor modeling,” “reporting” and “prospect identification,” too. Do these terms mean the same thing? What are the differences among them?

I asked Marianne Pelletier, who leads the HBG Analytics team, to help me put together a series of short articles designed to make sense of all of this. In each, we will describe the method and give examples of how they can be used. To begin our series, we discuss data mining.

Let’s begin with a case study:

The fundraising team at a university is having a problem with donor retention. Every year the university must acquire a significant number of new donors to offset the nearly 50% of donors they lose from the previous year. They need to find an answer to the question “why are we losing so many donors, and what can we do to keep them from leaving?”

They decide to use a technique called data mining to find out.

What is data mining?

Sometimes we don’t know what we don’t know. In those situations, it may be best to explore a little to see if we can find answers (and perhaps even see the questions we need to ask). Think of it as discovering what’s in a new mall by walking the length of it: data mining is like shopping in the data mall. What will we find when we look in each store?

Data mining sifts data back and forth until it finds natural breaking points, puts together associated characteristics and then lays out what it finds. For example:

“The donors who give the highest gift amounts are married male alumni. They’re in their 50s. They live along the east or west coast. They have a job and children that we know about.”

Data mining delves deeper to find relationships between many characteristics

The good news is that data mining also names the characteristics for the second best segment, and the third best, and so on, so a university could use it to find out various solutions to their attrition problem. The characteristics they see might look like these:

“The donors who stop donating tend to leave between their 5th and 6th years of consistently donating to the annual fund” So we can work harder to retain them before year 5!

“The donors who stop donating tend to be married alumni males in their thirties” What special incentives can we offer to that group since we know that married men in their 50s tend to be our largest donors later on?

Data mining can also be used for:

Determining the best solicitation methods for donor acquisition, renewal, or upgrade

Measuring the characteristics of event attendees who later become donors

Understanding the clusters of members/grateful patients/families/alumni/docents who respond better to e-mail, direct mail, phone calls, or social media

Finding the best pattern for the cultivation/giving ladder

Adding or dropping solicitation methods, or venues

Assessing timing, including how long it takes to successfully solicit gifts at different levels

What else can data mining do?

Have you ever walked into a store that has section after section of fun things for purchase? A department each of clever t-shirts, gifts that your best friend would love, beautiful hand-crafts, and more – things that are so perfect that you’ve picked up an armful of things and you need to find a basket to dump them all in.

Data mining is like that. It can also be for:

clustering like-minded, or like-attributed prospects for a cultivation dinner

breaking long-held “truths” about your donor base, such as “our best athletics prospects are male football alumni.” What if that’s not actually true?

looking at what statistics calls “interactions” – the combination of characteristics that make good prospects, members, volunteers, trustees, etc. For example: Married prospects and/or prospects living in rural areas show a mild relationship to loyal giving. However, prospects who are married AND live in rural areas show a strong relationship to loyal giving.

determining which group responds best to email and which to social media.

What do you want to know?

Our series continues next Thursday, September 12 when we will discuss Donor Modeling.

Do you have questions about data mining or would you like to see how it can work for your organization? Email us for more information at info [at] helenbrowngroup [dot] com.